Yi, Joseph Keunhong, Rim, Tyler Hyungtaek 
ORCID: 0000-0001-6465-2620, Park, Sungha ORCID: 0000-0001-5362-478X, Kim, Sung Soo, Kim, Hyeon Chang, Lee, Chan Joo, Kim, Hyeonmin, Lee, Geunyoung, Lim, James Soo Ghim, Tan, Yong Yu et al (show 8 more authors)
(2023)
Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores.
European heart journal. Digital health, 4 (3).
pp. 236-244.
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Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker a comparison with existing risk scores.pdf - Author Accepted Manuscript Download (1MB) | Preview |
Abstract
<h4>Aims</h4>This study aims to evaluate the ability of a deep-learning-based cardiovascular disease (CVD) retinal biomarker, Reti-CVD, to identify individuals with intermediate- and high-risk for CVD.<h4>Methods and results</h4>We defined the intermediate- and high-risk groups according to Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Reti-CVD's prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. In the UK Biobank, among 48 260 participants, 20 643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk groups according to PCE, and QRISK3, respectively. In the Singapore Epidemiology of Eye Diseases study, among 6810 participants, 3799 (55.8%) were classified as intermediate- and high-risk group according to modified FRS. Reti-CVD identified PCE-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.7%, 87.6%, 86.5%, and 84.0%, respectively. Reti-CVD identified QRISK3-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.6%, 85.5%, 49.9%, and 96.6%, respectively. Reti-CVD identified intermediate- and high-risk groups according to the modified FRS with a sensitivity, specificity, PPV, and NPV of 82.1%, 80.6%, 76.4%, and 85.5%, respectively.<h4>Conclusion</h4>The retinal photograph biomarker (Reti-CVD) was able to identify individuals with intermediate and high-risk for CVD, in accordance with existing risk assessment tools.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Cardiovascular disease, Deep learning, Reti-CVD, Retinal photograph, Risk stratification, Singapore Epidemiology of Eye Diseases, UK Biobank |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Population Health |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 13 Jun 2023 14:58 |
| Last Modified: | 13 Jun 2023 14:58 |
| DOI: | 10.1093/ehjdh/ztad023 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3170969 |
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